7 edition of New optimization algorithms in physics found in the catalog.
Includes bibliographical references and index.
|Statement||edited by Alexander K. Hartmann and Heiko Rieger.|
|Contributions||Hartmann, Alexander K., Rieger, Heiko.|
|LC Classifications||QC20.7.C58 N49 2004|
|The Physical Object|
|Pagination||xii, 300 p. :|
|Number of Pages||300|
|LC Control Number||2004558865|
the relationship of simulation optimization to math-ematical programming, derivative-free optimization, and machine learning. Section 2 provides a glimpse into the wide variety of applications of simulation optimization that have appeared in the literature. Section 3 focuses on various algorithms for discreteFile Size: KB. Ever increasingly practical and robust methods have been developed, and every new generation of computers with their increased power and speed allows for the development and wider application of new types of solutions. This book defines the fundamentals, background and theoretical concepts of optimization principles in a comprehensive manner.
The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms. Cited By Rao R and Saroj A () An elitism-based self-adaptive multi-population Jaya algorithm and its applications, Soft Computing - A Fusion of Foundations, Methodologies and Applications. The Physical Systems Behind Optimization Algorithms L. F. Yang, R. Arora, V. Braverman, and T. Zhao* Abstract We use diﬀerential equations based approaches to provide some physics insights into an- alyzing the dynamics of popular optimization algorithms in machine learning.
Book Title: New Optimization Techniques in Engineering Author(s): Godfrey C. Onwubolu, B.V. Babu Publisher: Springer Edition: First Pages: PDf Size: 14Mb Book Description: Presently, general-purpose optimization techniques such as Simulated Annealing, and Genetic Algorithms, have become standard optimization techniques. As we introduce new ideas into model design, model examination, model changes, global restriction handling and hybridization, the advantages of this new optimization method can be used without expert knowledge. With increased complex optimization problems, hybrid optimization calculations and programming language concepts become essential.
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Applications of optimization algorithms in physics Problems related to theoretical computer sciences Interdisciplinary applications and heuristics Alexander Hartmann studied computer science and physics at the universities of Hagen, Duisburg and Heidelberg, Germany.
After receiving his PhD inhe went as a postdoc first to the University Format: Hardcover. His main research areas are: statistical physics and computational physics, in particular disordered and glassy systems, non-equilibrium dynamics, stochastic processes, complex systems, Monte Carlo simulations and combinatorial optimization.
This transition probability is chosen to satisfy the fundamental condition of detailed balance π (a)P (a → b) = π (b)P (b → a) which is implemented using the Metropolis algorithm u u π (b) P (a → b) = min 1, π (a) or one of its variants.
New Optimization Algorithms in Physics. Get this from a library. New optimization algorithms in physics. [Alexander K Hartmann; Heiko Rieger;] -- Many physicists are not aware of the fact that they can solve their problems by applying optimization algorithms. Since the number of such algorithms is steadily increasing, many new algorithms have.
This book serves as an introduction to the field, while also presenting a complete overview of modern algorithms. The authors begin with the relevant foundations from computer science, graph theory and statistical physics, before moving on to thoroughly explain algorithms - backed by illustrative examples.
New Optimization Algorithms in Physics adds to the reader's knowledge. Everything you read will fill your head with new information, and you'll never know when it might be useful. The more knowledge you have, the better equipped to solve the problems you have faced.
John Wiley & Sons". New Optimization Algorithms in Physics Edited by Alexander nn and Heiko Rieger Titelei_Hartmann Uhr Seite 3 (Black/Process Black Bogen) Accepted set by 50I. None set by 50I. New Optimization Algorithms in Physics, A. Hartmann and H. Rieger (Wiley-VCH). Algorithms and Computations, W.
Krauth (Oxford University Press). Phase Transitions in Combinatorial Optimization Problems, A. Hartmann and M. Weigt (Wiley-VCH). Quantum Annealing and Related Optimization Methods, A. Das and B. Chakrabarti (Springer). Optimization – Theory and Algorithms By Jean Cea Tata Institute of Fundamental Research, Bombay ISBN Springer-Verlag Berlin, Heidelberg.
New York ISBN Springer-Verlag New York, Heidelberg,Berlin No part of this book may be reproduced in any form by print, microﬁlm or any other means with-out written Cited by: New Optimization Algorithms in Physics (US $)-and-Separable Boundary-Value Problems in Physics (US $) Total List Price: US $ Discounted Price: US $ (Save: US $).
Part I: Applications in Physics. 2 Cluster Monte Carlo Algorithms (W. Krauth). Detailed Balance and a priori Probabilities. The Wolff Cluster Algorithm for the Ising Model. Cluster Algorithm for Hard Spheres and Related Systems. Applications.
Phase Separation in Binary Mixtures. Polydisperse Mixtures. Monomer-Dimer Problem. Limitations and Extensions. Feasibility and Infeasibility in Optimization is a timely expository book that summarizes the state of the art in both classical and recent algorithms related to feasibility and infeasibility in optimization, with a focus on practical methods.
All model forms are covered, including linear. The contents of the book represent the fundamental optimization mate rial collected and used by the author, over a period of more than twenty years, in teaching Practical Mathematical Optimization to undergradu ate as well as graduate engineering and science students at the University of Size: 1MB.
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Skip to main content. LOGIN ; GET LIBRARY CARD ; GET EMAIL UPDATES ; SEARCH ; Home ; About Us. In this second edition, the emphasis remains on finite-dimensional optimization.
New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth. Natural phenomenon can be used to solve complex optimization problems with its excellent facts, functions, and phenomenon.
In this paper, a survey on physics-based algorithm is done to show how these inspirations led to the solution of well-known optimization problem. The survey is focused on inspirations that are originated from physics, their formulation into solutions, and their evolution Cited by: But as soon as a huge number of degrees of freedom are involved, as is typically the case in statistical physics, condensed matter, astrophysics and biophysics, conventional methods fail to find the optimum in a reasonable time and new methods have to be invented.
This book contains a representative collection of new optimization algorithms. This book describes new method of optimization (''Method of Deformation of Functional'') that has the advantages at greater generality and flexibility as well as the ability to solve complex problems which other methods cannot solve.
( views) Optimization Algorithms: Methods and Applications by Ozgur Baskan (ed.) - InTech, In: Hartmann A, Rieger H (eds) New optimization algorithms in physics.
Wiley, Berlin; Cocco S, Ein-Dor L, Monasson R (ibid) Analysis of backtracking procedures for random decision problems; Zecchina R (ibid) New iterative algorithms for hard combinatorial problems Google Scholar. Teaching Learning Based Optimization Algorithm Pdf Describing a new optimization algorithm, the more”Teaching-Learning-Based optimization (TLBO),” in a very clear and lucid manner, this publication provokes reader insights into the way in which the TLBO algorithm may be utilized to fix continuous and discrete optimization problems between single or multiple goals.Optimization Algorithms: Methods and Applications by Ozgur Baskan (ed.).
Publisher: InTech ISBN Number of pages: Description: This book covers state-of-the-art optimization methods and their applications in wide range especially for researchers and practitioners who wish to improve their knowledge in this field.New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python.
A special Python module is electronically available (via springerlink) that makes the new algorithms featured in /5(2).